Empirical correlation of PGA, spectral accelerations and spectrum intensities from active shallow crustal earthquakes

Bradley, Brendon

doi:10.1002/eqe.1110

Cited by 54 publications

(64 citation statements)

References 33 publications

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“…This is a similar behavior to most published studies about correlations of the response spectrum at different periods [e.g. Baker and Jayaram (2008), Bradley (2011Bradley ( , 2012]. In Fig.…”

Section: Correlationssupporting

confidence: 84%

“…6, the mean correlations between PGA, PGV and the spectral accelerations are shown. For comparison, the models of Bradley (2011Bradley ( , 2012 are also plotted. Since these models are valid for the total correlation, we calculate the total correlation from the between-event, between-station and record-to-record correlation by …”

Section: Correlationsmentioning

confidence: 99%

“…Baker and Jayaram 2008;Bradley 2011Bradley , 2012. These are important for the estimation of joint hazard (Bazzurro and Cornell 2002) or for the conditional mean spectrum Baker 2011).…”

Section: Introductionmentioning

confidence: 99%

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Ground-motion prediction model building: a multilevel approach

Kuehn

Scherbaum

2015

Bull Earthquake Eng

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A Bayesian ground-motion model is presented that directly estimates the coefficients of the model and the correlation between different ground-motion parameters of interest. The model is developed as a multi-level model with levels for earthquake, station and record terms. This separation allows to estimate residuals for each level and thus the estimation of the associated aleatory variability. In particular, the usually estimated withinevent variability is split into a between-station and between-record variability. In addition, the covariance structure between different ground-motion parameters of interest is estimated for each level, i.e. directly the between-event, between-station and between-record correlation coefficients are available. All parameters of the model are estimated via Bayesian inference, which allows to assess their epistemic uncertainty in a principled way. The model is developed using a recently compiled European strong-motion database. The target variables are peak ground velocity, peak ground acceleration and spectral acceleration at eight oscillator periods. The model performs well with respect to its residuals, and is similar to other ground-motion models using the same underlying database. The correlation coefficients are similar to those estimated for other parts of the world, with nearby periods having a high correlation. The between-station, between-event and between-record correlations follow generally a similar trend.

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Section: Correlationssupporting

confidence: 84%

Section: Correlationsmentioning

confidence: 99%

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Ground-motion prediction model building: a multilevel approach

Kuehn

Scherbaum

2015

Bull Earthquake Eng

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“…10a illustrates the parametric form of the empirical correlation between AI and SA developed in this study (i.e., Eqs. (10) and (11)), as well as empirical correlations of CAV and PGA with SA, as presented in Bradley [52] and Bradley [53], respectively. It can be seen that AI is more highly correlated with SA than CAV for vibration periods less than approximately T ¼ 1 s, and vice versa, as also found by Campbell and Bozorgnia [10].…”

Section: Differences Between Empirical Correlations With Ai and Cav Amentioning

confidence: 98%

Correlation of Arias intensity with amplitude, duration and cumulative intensity measures

Bradley

2015

Soil Dynamics and Earthquake Engineering

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“…In other words, ε IM is the number of standard deviations by which an observed ln IM differs from the predicted mean (μ ln IM ). As pointed out by Bradley (2011b and2012), due to the linear relationship between ln IM and ε IM , the correlation between the logarithms of two IMs for a given earthquake rupture is equal to the correlation between their epsilons. Hence, to obtain the correlation between ln S a (T) and ln (S a (T)/DSI), ρ ln Sa(T), ln (Sa(T)/DSI) , the correlation between ε Sa and ε Bradley (2011a) were applied for obtaining the means and standard deviations required in Eqs.…”

mentioning

confidence: 97%

Optimal vector-valued intensity measure for seismic collapse assessment of structures

Yakhchalian

Nicknam

Amiri

2015

Earthq. Eng. Eng. Vib.

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The present study is aimed to investigate the ability of different intensity measures (IMs), including response spectral acceleration at the fundamental period of the structure, S a (T 1 ), as a common scalar IM and twelve vector-valued IMs for seismic collapse assessment of structures. The vector-valued IMs consist of two components, with S a (T 1 ) as the fi rst component and different parameters that are ratios of scalar IMs, as well as the spectral shape proxies ε Sa and N p , as the second component. After investigating the properties of an optimal IM, a new vector-valued IM that includes the ratio of S a (T 1 ) to the displacement spectrum intensity (DSI) as the second component is proposed. The new IM is more effi cient than other IMs for predicting the collapse capacity of structures. It is also suffi cient with respect to magnitude, source-to-site distance, and scale factor for collapse capacity prediction of structures. To satisfy the predictability criterion, a ground motion prediction equation (GMPE) is determined for S a (T 1 )/DSI by using the existing GMPEs. Furthermore, an empirical equation is proposed for obtaining the correlation between the components of the proposed IM. The results of this study show that using the new vector-valued IM leads to a more reliable seismic collapse assessment of structures.

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Empirical correlation of PGA, spectral accelerations and spectrum intensities from active shallow crustal earthquakes

Cited by 54 publications

References 33 publications

Ground-motion prediction model building: a multilevel approach

Ground-motion prediction model building: a multilevel approach

Correlation of Arias intensity with amplitude, duration and cumulative intensity measures

Optimal vector-valued intensity measure for seismic collapse assessment of structures

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